
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
import plotly.express as px

pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 5000)


tic = pd.read_csv("./data/ticdata2000.txt", sep = "\s+", header= None)


 print(tic.shape)

(5822, 86)


tic.columns = [ 'MOSTYPE', 'MAANTHUI', 'MGEMOMV', 'MGEMLEEF', 'MOSHOOFD', 'MGODRK', 'MGODPR', 'MGODOV', 'MGODGE', 'MRELGE',
                   'MRELSA', 'MRELOV', 'MFALLEEN', 'MFGEKIND', 'MFWEKIND', 'MOPLHOOG', 'MOPLMIDD', 'MOPLLAAG', 'MBERHOOG', 
                   'MBERZELF', 'MBERBOER', 'MBERMIDD', 'MBERARBG', 'MBERARBO', 'MSKA', 'MSKB1', 'MSKB2', 'MSKC', 'MSKD', 
                   'MHHUUR', 'MHKOOP', 'MAUT1', 'MAUT2', 'MAUT0', 'MZFONDS', 'MZPART', 'MINKM30', 'MINK3045', 'MINK4575',
                   'MINK7512', 'MINK123M', 'MINKGEM', 'MKOOPKLA', 'PWAPART', 'PWABEDR', 'PWALAND', 'PPERSAUT','PBESAUT', 
                   'PMOTSCO', 'PVRAAUT', 'PAANHANG', 'PTRACTOR', 'PWERKT', 'PBROM', 'PLEVEN', 'PPERSONG', 'PGEZONG', 'PWAOREG', 
                   'PBRAND', 'PZEILPL', 'PPLEZIER', 'PFIETS', 'PINBOED', 'PBYSTAND', 'AWAPART', 'AWABEDR', 'AWALAND', 
                   'APERSAUT', 'ABESAUT', 'AMOTSCO', 'AVRAAUT', 'AAANHANG', 'ATRACTOR', 'AWERKT', 'ABROM', 'ALEVEN', 
                   'APERSONG', 'AGEZONG', 'AWAOREG', 'ABRAND', 'AZEILPL', 'APLEZIER', 'AFIETS', 'AINBOED', 'ABYSTAND',
                   'CARAVAN']


#Dimensión de la tabla y valores duplicados
print(tic.shape, tic.drop_duplicates().shape)

(5822, 86) (5220, 86)


tic[tic.duplicated()]
#Decido no eliminar los duplicados porque hay clientes que pueden tener las mismas características


#En el data frame vienen datos relacionados con la religion de los clientes, 
#como es ilegal ultilizar este tipo de variables las tenemos que quitar.
tic = tic.drop(['MGODRK', 'MGODPR', 'MGODOV', 'MGODGE'], axis=1)


#Necesitamos saber de que tipo es cada variable para saber como tratar con ellas.
#En este caso, vemos que la variable objetivo CARAVAN es un integer.
tic.dtypes.to_dict()

{'MOSTYPE': dtype('int64'),
 'MAANTHUI': dtype('int64'),
 'MGEMOMV': dtype('int64'),
 'MGEMLEEF': dtype('int64'),
 'MOSHOOFD': dtype('int64'),
 'MRELGE': dtype('int64'),
 'MRELSA': dtype('int64'),
 'MRELOV': dtype('int64'),
 'MFALLEEN': dtype('int64'),
 'MFGEKIND': dtype('int64'),
 'MFWEKIND': dtype('int64'),
 'MOPLHOOG': dtype('int64'),
 'MOPLMIDD': dtype('int64'),
 'MOPLLAAG': dtype('int64'),
 'MBERHOOG': dtype('int64'),
 'MBERZELF': dtype('int64'),
 'MBERBOER': dtype('int64'),
 'MBERMIDD': dtype('int64'),
 'MBERARBG': dtype('int64'),
 'MBERARBO': dtype('int64'),
 'MSKA': dtype('int64'),
 'MSKB1': dtype('int64'),
 'MSKB2': dtype('int64'),
 'MSKC': dtype('int64'),
 'MSKD': dtype('int64'),
 'MHHUUR': dtype('int64'),
 'MHKOOP': dtype('int64'),
 'MAUT1': dtype('int64'),
 'MAUT2': dtype('int64'),
 'MAUT0': dtype('int64'),
 'MZFONDS': dtype('int64'),
 'MZPART': dtype('int64'),
 'MINKM30': dtype('int64'),
 'MINK3045': dtype('int64'),
 'MINK4575': dtype('int64'),
 'MINK7512': dtype('int64'),
 'MINK123M': dtype('int64'),
 'MINKGEM': dtype('int64'),
 'MKOOPKLA': dtype('int64'),
 'PWAPART': dtype('int64'),
 'PWABEDR': dtype('int64'),
 'PWALAND': dtype('int64'),
 'PPERSAUT': dtype('int64'),
 'PBESAUT': dtype('int64'),
 'PMOTSCO': dtype('int64'),
 'PVRAAUT': dtype('int64'),
 'PAANHANG': dtype('int64'),
 'PTRACTOR': dtype('int64'),
 'PWERKT': dtype('int64'),
 'PBROM': dtype('int64'),
 'PLEVEN': dtype('int64'),
 'PPERSONG': dtype('int64'),
 'PGEZONG': dtype('int64'),
 'PWAOREG': dtype('int64'),
 'PBRAND': dtype('int64'),
 'PZEILPL': dtype('int64'),
 'PPLEZIER': dtype('int64'),
 'PFIETS': dtype('int64'),
 'PINBOED': dtype('int64'),
 'PBYSTAND': dtype('int64'),
 'AWAPART': dtype('int64'),
 'AWABEDR': dtype('int64'),
 'AWALAND': dtype('int64'),
 'APERSAUT': dtype('int64'),
 'ABESAUT': dtype('int64'),
 'AMOTSCO': dtype('int64'),
 'AVRAAUT': dtype('int64'),
 'AAANHANG': dtype('int64'),
 'ATRACTOR': dtype('int64'),
 'AWERKT': dtype('int64'),
 'ABROM': dtype('int64'),
 'ALEVEN': dtype('int64'),
 'APERSONG': dtype('int64'),
 'AGEZONG': dtype('int64'),
 'AWAOREG': dtype('int64'),
 'ABRAND': dtype('int64'),
 'AZEILPL': dtype('int64'),
 'APLEZIER': dtype('int64'),
 'AFIETS': dtype('int64'),
 'AINBOED': dtype('int64'),
 'ABYSTAND': dtype('int64'),
 'CARAVAN': dtype('int64')}


#Analizamos los valores nulos 
tic.isnull().sum().sort_values()
#Ninguna variable tiene valores nulos

MOSTYPE     0
PINBOED     0
PFIETS      0
PPLEZIER    0
PZEILPL     0
PBRAND      0
PWAOREG     0
PGEZONG     0
PBYSTAND    0
PPERSONG    0
PBROM       0
PWERKT      0
PTRACTOR    0
PAANHANG    0
PVRAAUT     0
PMOTSCO     0
PBESAUT     0
PLEVEN      0
AWAPART     0
AWABEDR     0
AWALAND     0
AINBOED     0
AFIETS      0
APLEZIER    0
AZEILPL     0
ABRAND      0
AWAOREG     0
AGEZONG     0
APERSONG    0
ALEVEN      0
ABROM       0
AWERKT      0
ATRACTOR    0
AAANHANG    0
AVRAAUT     0
AMOTSCO     0
ABESAUT     0
APERSAUT    0
PPERSAUT    0
PWALAND     0
PWABEDR     0
PWAPART     0
MBERMIDD    0
MBERBOER    0
MBERZELF    0
MBERHOOG    0
MOPLLAAG    0
MOPLMIDD    0
MOPLHOOG    0
MFWEKIND    0
MFGEKIND    0
MFALLEEN    0
MRELOV      0
MRELSA      0
MRELGE      0
MOSHOOFD    0
MGEMLEEF    0
MGEMOMV     0
MAANTHUI    0
MBERARBG    0
ABYSTAND    0
MBERARBO    0
MSKB1       0
MKOOPKLA    0
MINKGEM     0
MINK123M    0
MINK7512    0
MINK4575    0
MINK3045    0
MINKM30     0
MZPART      0
MZFONDS     0
MAUT0       0
MAUT2       0
MAUT1       0
MHKOOP      0
MHHUUR      0
MSKD        0
MSKC        0
MSKB2       0
MSKA        0
CARAVAN     0
dtype: int64


tic_status = tic['CARAVAN']\
        .value_counts(normalize=True)\
        .mul(100).rename('percent').reset_index()

tic_status_conteo = tic['CARAVAN'].value_counts().reset_index()
tic_status_pc = pd.merge(tic_status, tic_status_conteo, on=['index'], how='inner')
tic_status_pc


fig = px.histogram(tic_status_pc, x='index', y=['percent'])
fig.show()


tic.describe()


#Vemos cuantos valores unicos hay en cada variable
for i, var in enumerate(tic.columns):
    print(i,sorted(tic[var].unique()))

0 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41]
1 [1, 2, 3, 4, 5, 6, 7, 8, 10]
2 [1, 2, 3, 4, 5]
3 [1, 2, 3, 4, 5, 6]
4 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
5 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
6 [0, 1, 2, 3, 4, 5, 6, 7]
7 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
8 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
9 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
10 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
11 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
12 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
13 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
14 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
15 [0, 1, 2, 3, 4, 5]
16 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
17 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
18 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
19 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
20 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
21 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
22 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
23 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
24 [0, 1, 2, 3, 4, 5, 6, 7, 9]
25 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
26 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
27 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
28 [0, 1, 2, 3, 4, 5, 6, 7]
29 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
30 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
31 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
32 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
33 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
34 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
35 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
36 [0, 1, 2, 3, 4, 5, 7, 9]
37 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
38 [1, 2, 3, 4, 5, 6, 7, 8]
39 [0, 1, 2, 3]
40 [0, 1, 2, 3, 4, 5, 6]
41 [0, 2, 3, 4]
42 [0, 4, 5, 6, 7, 8]
43 [0, 5, 6, 7]
44 [0, 3, 4, 5, 6, 7]
45 [0, 4, 6, 9]
46 [0, 1, 2, 3, 4, 5]
47 [0, 3, 4, 5, 6]
48 [0, 2, 3, 4, 6]
49 [0, 2, 3, 4, 5, 6]
50 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
51 [0, 1, 2, 3, 4, 5, 6]
52 [0, 2, 3]
53 [0, 4, 5, 6, 7]
54 [0, 1, 2, 3, 4, 5, 6, 7, 8]
55 [0, 1, 3]
56 [0, 1, 2, 3, 4, 5, 6]
57 [0, 1]
58 [0, 1, 2, 3, 4, 5, 6]
59 [0, 2, 3, 4, 5]
60 [0, 1, 2]
61 [0, 1, 5]
62 [0, 1]
63 [0, 1, 2, 3, 4, 6, 7]
64 [0, 1, 2, 3, 4]
65 [0, 1, 2, 8]
66 [0, 1, 2, 3]
67 [0, 1, 2, 3]
68 [0, 1, 2, 3, 4]
69 [0, 1, 2, 3, 6]
70 [0, 1, 2]
71 [0, 1, 2, 3, 4, 8]
72 [0, 1]
73 [0, 1]
74 [0, 1, 2]
75 [0, 1, 2, 3, 4, 5, 7]
76 [0, 1]
77 [0, 1, 2]
78 [0, 1, 2, 3]
79 [0, 1, 2]
80 [0, 1, 2]
81 [0, 1]


def dame_variables_categoricas(dataset=None):
   
    if dataset is None:
        print(u'\nFaltan argumentos por pasar a la función')
        return 1
    lista_variables_categoricas = []
    other = []
    for i in dataset.columns:
        if (dataset[i].dtype!=float) & (dataset[i].dtype!=int):
            unicos = int(len(np.unique(dataset[i].dropna(axis=0, how='all'))))
            if unicos < 10:
                lista_variables_categoricas.append(i)
            else:
                other.append(i)

    return lista_variables_categoricas, other


categoricas, other = dame_variables_categoricas(tic)
print(len(categoricas), len(other))

51 31


tic_numericas = tic[['MAANTHUI', 'MFGEKIND', 'MFWEKIND', 'MHHUUR', 'MHKOOP', 'MAUT1', 
             'MAUT2', 'MAUT0', 'AWAPART', 'AWABEDR', 'AWALAND', 'APERSAUT', 'ABESAUT',
             'AMOTSCO', 'AVRAAUT', 'AAANHANG', 'ATRACTOR', 'AWERKT', 'ABROM', 'ALEVEN',
             'APERSONG', 'AGEZONG', 'AWAOREG', 'ABRAND', 'AZEILPL', 'APLEZIER', 'AFIETS', 'AINBOED', 'ABYSTAND']]
tic_numericas.shape
#29 variables continuas

(5822, 29)


tic_categoricas = tic.drop(['MAANTHUI', 'MFGEKIND', 'MFWEKIND', 'MHHUUR', 'MHKOOP', 'MAUT1', 
             'MAUT2', 'MAUT0', 'AWAPART', 'AWABEDR', 'AWALAND', 'APERSAUT', 'ABESAUT',
             'AMOTSCO', 'AVRAAUT', 'AAANHANG', 'ATRACTOR', 'AWERKT', 'ABROM', 'ALEVEN',
             'APERSONG', 'AGEZONG', 'AWAOREG', 'ABRAND', 'AZEILPL', 'APLEZIER', 'AFIETS', 'AINBOED', 'ABYSTAND'], axis=1)
tic_categoricas.shape
#57 variables categóricas

(5822, 53)


def plot_feature(df, col_name, isContinuous, target):
    """
    Visualize a variable with and without faceting on the loan status.
    - df dataframe
    - col_name is the variable name in the dataframe
    - full_name is the full variable name
    - continuous is True if the variable is continuous, False otherwise
    """
    f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,3), dpi=90)
    
    count_null = df[col_name].isnull().sum()
    if isContinuous:
        
        sns.histplot(df.loc[df[col_name].notnull(), col_name], kde=False, ax=ax1)
    else:
        sns.countplot(df[col_name], order=sorted(df[col_name].unique()), color='#5975A4', saturation=1, ax=ax1)
    ax1.set_xlabel(col_name)
    ax1.set_ylabel('Count')
    ax1.set_title(col_name+ ' Numero de nulos: '+str(count_null))
    plt.xticks(rotation = 90)


    if isContinuous:
        sns.boxplot(x=col_name, y=target, data=df, ax=ax2)
        ax2.set_ylabel('')
        ax2.set_title(col_name + ' by '+target)
    else:
        data = df.groupby(col_name)[target].value_counts(normalize=True).to_frame('proportion').reset_index() 
        data.columns = [i, target, 'proportion']
        #sns.barplot(x = col_name, y = 'proportion', hue= target, data = data, saturation=1, ax=ax2)
        sns.barplot(x = col_name, y = 'proportion', hue= target, data = data, saturation=1, ax=ax2)
        ax2.set_ylabel(target+' fraction')
        ax2.set_title(target)
        plt.xticks(rotation = 90)
    ax2.set_xlabel(col_name)
    
    plt.tight_layout()


# warnings.filterwarnings('ignore')
for i in list(tic.columns):
    if (tic[i].dtype==float) & (i!='CARAVAN'):
        plot_feature(tic, col_name=i, isContinuous=True, target='CARAVAN')
    elif  i!='CARAVAN':
        plot_feature(tic, col_name=i, isContinuous=False, target='CARAVAN')

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\ipykernel_launcher.py:9: RuntimeWarning:

More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.


sns.boxplot(tic['AWABEDR'])

C:\Users\Usuario\OneDrive\Documentos\anaconda\envs\mlp\lib\site-packages\seaborn\_decorators.py:43: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

<AxesSubplot:xlabel='AWABEDR'>


sns.histplot(tic['APERSAUT'])

<AxesSubplot:xlabel='APERSAUT', ylabel='Count'>


sns.histplot(tic['ALEVEN'])

<AxesSubplot:xlabel='ALEVEN', ylabel='Count'>


def get_deviation_of_mean_perc(tic, tic_numericas, target, multiplier):
    """
    Devuelve el porcentaje de valores que exceden del intervalo de confianza
    :type series:
    :param multiplier:
    :return:
    """
    pd_final = pd.DataFrame()
    
    for i in tic_numericas:
        
        series_mean = tic[i].mean()
        series_std = tic[i].std()
        std_amp = multiplier * series_std
        left = series_mean - std_amp
        right = series_mean + std_amp
        size_s = tic[i].size
        
        perc_goods = tic[i][(tic[i] >= left) & (tic[i] <= right)].size/size_s
        perc_excess = tic[i][(tic[i] < left) | (tic[i] > right)].size/size_s
        
        if perc_excess>0:    
            pd_concat_percent = pd.DataFrame(tic[target][(tic[i] < left) | (tic[i] > right)]\
                                            .value_counts(normalize=True).reset_index()).T
            print('========', i)
            print('pd_concat_percent', pd_concat_percent.shape, pd_concat_percent.columns)
            print(pd_concat_percent.iloc[0,0])
            if pd_concat_percent.shape[1] == 1:
                pd_concat_percent['1'] = [1.0, 0.0]
            else:
                print(pd_concat_percent.iloc[0,1])
            print(pd_concat_percent)
            
            pd_concat_percent.columns = [pd_concat_percent.iloc[0,0], 
                                         pd_concat_percent.iloc[0,1]]
            pd_concat_percent = pd_concat_percent.drop('index',axis=0)
            pd_concat_percent['variable'] = i
            pd_concat_percent['sum_outlier_values'] = tic[i][(tic[i] < left) | (tic[i] > right)].size
            pd_concat_percent['porcentaje_sum_outliers_values'] = perc_excess
            pd_final = pd.concat([pd_final, pd_concat_percent], axis=0).reset_index(drop=True)
            
    if pd_final.empty:
        print('No existen variables con valores nulos')
        
    return pd_final


get_deviation_of_mean_perc(tic, tic_numericas, target='CARAVAN', multiplier=2)

======== MAANTHUI
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.940541  0.059459
======== MFGEKIND
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.928058  0.071942
======== MFWEKIND
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.943953  0.056047
======== MAUT1
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.989011  0.010989
======== MAUT2
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
               0        1
index    0.00000  1.00000
CARAVAN  0.93733  0.06267
======== MAUT0
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.965278  0.034722
======== AWAPART
pd_concat_percent (2, 1) RangeIndex(start=0, stop=1, step=1)
0.0
           0    1
index    0.0  1.0
CARAVAN  1.0  0.0
======== AWABEDR
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.939024  0.060976
======== AWALAND
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
             0      1
index    0.000  1.000
CARAVAN  0.975  0.025
======== APERSAUT
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
               0        1
index    0.00000  1.00000
CARAVAN  0.85283  0.14717
======== ABESAUT
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.958333  0.041667
======== AMOTSCO
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.927928  0.072072
======== AVRAAUT
pd_concat_percent (2, 1) RangeIndex(start=0, stop=1, step=1)
0.0
           0    1
index    0.0  1.0
CARAVAN  1.0  0.0
======== AAANHANG
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.907692  0.092308
======== ATRACTOR
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.965035  0.034965
======== AWERKT
pd_concat_percent (2, 1) RangeIndex(start=0, stop=1, step=1)
0.0
           0    1
index    0.0  1.0
CARAVAN  1.0  0.0
======== ABROM
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.979798  0.020202
======== ALEVEN
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.921502  0.078498
======== APERSONG
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.967742  0.032258
======== AGEZONG
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.842105  0.157895
======== AWAOREG
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.826087  0.173913
======== ABRAND
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
               0        1
index    0.00000  1.00000
CARAVAN  0.94964  0.05036
======== AZEILPL
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.666667  0.333333
======== APLEZIER
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.606061  0.393939
======== AFIETS
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.897959  0.102041
======== AINBOED
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.888889  0.111111
======== ABYSTAND
pd_concat_percent (2, 2) RangeIndex(start=0, stop=2, step=1)
0.0
1.0
                0         1
index    0.000000  1.000000
CARAVAN  0.804878  0.195122


def get_corr_matrix(dataset = None, metodo='pearson', size_figure=[10,8]):
    
    if dataset is None:
        print(u'\nHace falta pasar argumentos a la función')
        return 1
    sns.set(style="white")
    # Compute the correlation matrix
    corr = dataset.corr(method=metodo) 
    # Set self-correlation to zero to avoid distraction
    for i in range(corr.shape[0]):
        corr.iloc[i, i] = 0
    # Set up the matplotlib figure
    f, ax = plt.subplots(figsize=size_figure)
    # Draw the heatmap with the mask and correct aspect ratio
    sns.heatmap(corr, center=0,
                square=True, linewidths=.5,  cmap ='viridis' ) #cbar_kws={"shrink": .5}
    plt.show()
    
    return 0


get_corr_matrix(dataset = tic_numericas,
                metodo='pearson', size_figure=[10,8])

0


sns.countplot(x='MGEMLEEF', data=tic)

<AxesSubplot:xlabel='MGEMLEEF', ylabel='count'>


sns.countplot(x='MOSTYPE', data=tic)

<AxesSubplot:xlabel='MOSTYPE', ylabel='count'>


sns.countplot(x='AZEILPL', data=tic)

<AxesSubplot:xlabel='AZEILPL', ylabel='count'>


import scipy.stats as ss
def cramers_v(confusion_matrix):
    """ 
    calculate Cramers V statistic for categorial-categorial association.
    uses correction from Bergsma and Wicher,
    Journal of the Korean Statistical Society 42 (2013): 323-328
    
    confusion_matrix: tabla creada con pd.crosstab()
    
    """
    chi2 = ss.chi2_contingency(confusion_matrix)[0]
    n = confusion_matrix.sum()
    phi2 = chi2 / n
    r, k = confusion_matrix.shape
    phi2corr = max(0, phi2 - ((k-1)*(r-1))/(n-1))
    rcorr = r - ((r-1)**2)/(n-1)
    kcorr = k - ((k-1)**2)/(n-1)
    return np.sqrt(phi2corr / min((kcorr-1), (rcorr-1)))


for i, var in enumerate(tic_categoricas.columns):
    confusion_matrix = pd.crosstab(tic_categoricas["CARAVAN"], tic_categoricas[var])
    print(i, cramers_v(confusion_matrix.values))

0 0.1214125245618953
1 0.030243996590796893
2 0.0
3 0.11698274512917237
4 0.06133135469764114
5 0.011961420404819938
6 0.05681982799852275
7 0.0392107064097822
8 0.08119222241325871
9 0.040821845823858366
10 0.09124021919390536
11 0.07271458978526386
12 0.0
13 0.043806512275053534
14 0.05268784814409118
15 0.06299303482198562
16 0.04994344919542805
17 0.08608184968100004
18 0.04532307942438214
19 0.0
20 0.0662220128865837
21 0.05578407787614905
22 0.05469114120189934
23 0.052551222491671
24 0.08617595715350806
25 0.0
26 0.0716795204794355
27 0.056539242337743115
28 0.0
29 0.10549998738432245
30 0.11417835774021888
31 0.0967392319560506
32 0.0
33 0.0
34 0.1805172111397274
35 0.0
36 0.05665441072703155
37 0.0
38 0.0
39 0.0
40 0.0
41 0.0367676124226097
42 0.015145064204040964
43 0.0
44 0.04623224495705996
45 0.026075963384164017
46 0.15081040147537925
47 0.02919647283588463
48 0.11346381331047402
49 0.02290268344531061
50 0.0
51 0.06600692666455492
52 0.998471614741666


def get_corr_matrix(dataset = None, metodo='spearman', size_figure=[10,8]):
    # Para obtener la correlación de Spearman, sólo cambiar el metodo por 'spearman'

    if dataset is None:
        print(u'\nHace falta pasar argumentos a la función')
        return 1
    sns.set(style="white")
    # Compute the correlation matrix
    corr = dataset.corr(method=metodo) 
    # Set self-correlation to zero to avoid distraction
    for i in range(corr.shape[0]):
        corr.iloc[i, i] = 0
    # Set up the matplotlib figure
    f, ax = plt.subplots(figsize=size_figure)
    # Draw the heatmap with the mask and correct aspect ratio
    sns.heatmap(corr, center=0,
                square=True, linewidths=.5,  cmap ='viridis' ) #cbar_kws={"shrink": .5}
    plt.show()
    
    return 0


get_corr_matrix(dataset = tic, 
                metodo='spearman', size_figure=[10,8])

0

	MOSTYPE	MAANTHUI	MGEMOMV	MGEMLEEF	MOSHOOFD	MRELGE	MRELSA	MRELOV	MFALLEEN	MFGEKIND	MFWEKIND	MOPLHOOG	MOPLMIDD	MOPLLAAG	MBERHOOG	MBERZELF	MBERBOER	MBERMIDD	MBERARBG	MBERARBO	MSKA	MSKB1	MSKB2	MSKC	MSKD	MHHUUR	MHKOOP	MAUT1	MAUT2	MAUT0	MZFONDS	MZPART	MINKM30	MINK3045	MINK4575	MINK7512	MINK123M	MINKGEM	MKOOPKLA	PWAPART	PWABEDR	PWALAND	PPERSAUT	PBESAUT	PMOTSCO	PVRAAUT	PAANHANG	PTRACTOR	PWERKT	PBROM	PLEVEN	PPERSONG	PGEZONG	PWAOREG	PBRAND	PZEILPL	PPLEZIER	PFIETS	PINBOED	PBYSTAND	AWAPART	AWABEDR	AWALAND	APERSAUT	ABESAUT	AMOTSCO	AVRAAUT	AAANHANG	ATRACTOR	AWERKT	ABROM	ALEVEN	APERSONG	AGEZONG	AWAOREG	ABRAND	AZEILPL	APLEZIER	AFIETS	AINBOED	ABYSTAND	CARAVAN
count	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.00000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000	5822.000000
mean	24.253349	1.110615	2.678805	2.991240	5.773617	6.183442	0.883545	2.290450	1.887667	3.230333	4.300240	1.461010	3.351254	4.572484	1.895053	0.397973	0.522329	2.899004	2.219856	2.306424	1.620749	1.606836	2.202508	3.758674	1.067331	4.236860	4.771728	6.040364	1.316386	1.959464	6.277053	2.728959	2.573686	3.536070	2.731364	0.796118	0.202679	3.784438	4.236345	0.771213	0.040021	0.071625	2.970457	0.048265	0.175369	0.009447	0.020955	0.092580	0.013054	0.215046	0.194778	0.013741	0.015287	0.023531	1.827722	0.000859	0.018894	0.025249	0.01563	0.047578	0.402954	0.014772	0.020611	0.562178	0.010477	0.041051	0.002233	0.012539	0.033665	0.006183	0.070423	0.076606	0.005325	0.006527	0.004638	0.570079	0.000515	0.006012	0.031776	0.007901	0.014256	0.059773
std	12.846706	0.405842	0.789835	0.814589	2.856760	1.909482	0.965924	1.722645	1.799928	1.619973	2.005283	1.622967	1.761052	2.298125	1.798321	0.775073	1.056926	1.839675	1.730840	1.692843	1.722882	1.330664	1.529319	1.935568	1.303175	3.089302	3.089837	1.552799	1.203072	1.599714	1.978675	1.981893	2.086099	1.882656	1.927738	1.162829	0.551557	1.317783	2.007150	0.958623	0.362680	0.499980	2.920669	0.531346	0.897222	0.244675	0.212738	0.603076	0.228906	0.813133	0.898100	0.209260	0.192471	0.375274	1.879290	0.043462	0.273028	0.156894	0.20456	0.409016	0.492631	0.134133	0.142092	0.604767	0.129991	0.228974	0.062819	0.125775	0.240755	0.124189	0.265112	0.377569	0.072782	0.080532	0.077403	0.562058	0.022696	0.081632	0.210986	0.090463	0.119996	0.237087
min	1.000000	1.000000	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	10.000000	1.000000	2.000000	2.000000	3.000000	5.000000	0.000000	1.000000	0.000000	2.000000	3.000000	0.000000	2.000000	3.000000	0.000000	0.000000	0.000000	2.000000	1.000000	1.000000	0.000000	1.000000	1.000000	2.000000	0.000000	2.000000	2.000000	5.000000	0.000000	1.000000	5.000000	1.000000	1.000000	2.000000	1.000000	0.000000	0.000000	3.000000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	30.000000	1.000000	3.000000	3.000000	7.000000	6.000000	1.000000	2.000000	2.000000	3.000000	4.000000	1.000000	3.000000	5.000000	2.000000	0.000000	0.000000	3.000000	2.000000	2.000000	1.000000	2.000000	2.000000	4.000000	1.000000	4.000000	5.000000	6.000000	1.000000	2.000000	7.000000	2.000000	2.000000	4.000000	3.000000	0.000000	0.000000	4.000000	4.000000	0.000000	0.000000	0.000000	5.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	35.000000	1.000000	3.000000	3.000000	8.000000	7.000000	1.000000	3.000000	3.000000	4.000000	6.000000	2.000000	4.000000	6.000000	3.000000	1.000000	1.000000	4.000000	3.000000	3.000000	2.000000	2.000000	3.000000	5.000000	2.000000	7.000000	7.000000	7.000000	2.000000	3.000000	8.000000	4.000000	4.000000	5.000000	4.000000	1.000000	0.000000	4.000000	6.000000	2.000000	0.000000	0.000000	6.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	4.000000	0.000000	0.000000	0.000000	0.00000	0.000000	1.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	41.000000	10.000000	5.000000	6.000000	10.000000	9.000000	7.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	5.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	7.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	9.000000	8.000000	3.000000	6.000000	4.000000	8.000000	7.000000	7.000000	9.000000	5.000000	6.000000	6.000000	6.000000	9.000000	6.000000	3.000000	7.000000	8.000000	3.000000	6.000000	1.000000	6.00000	5.000000	2.000000	5.000000	1.000000	7.000000	4.000000	8.000000	3.000000	3.000000	4.000000	6.000000	2.000000	8.000000	1.000000	1.000000	2.000000	7.000000	1.000000	2.000000	3.000000	2.000000	2.000000	1.000000

Lectura de archivos¶

Análisis de los DataFrame¶

Cargamos las variables y las añadimos al Dataframe¶

Análisis de la variable objetivo y tratamiento. 'CARAVAN'¶

Análisis de las variables¶

Vamos ha dividir el análisis de las variables según sean númericas o categóricas¶

Análisis variables númericas¶

Análisis de correlación entre la variable CARAVAN y el resto de variables categóricas.¶

Análisis variables categóricas¶

	0.0	1.0	variable	sum_outlier_values	porcentaje_sum_outliers_values
0	0.940541	0.059459	MAANTHUI	555	0.095328
1	0.928058	0.071942	MFGEKIND	139	0.023875
2	0.943953	0.056047	MFWEKIND	339	0.058227
3	0.989011	0.010989	MAUT1	91	0.015630
4	0.937330	0.062670	MAUT2	367	0.063037
5	0.965278	0.034722	MAUT0	144	0.024734
6	1.000000	0.000000	AWAPART	6	0.001031
7	0.939024	0.060976	AWABEDR	82	0.014085
8	0.975000	0.025000	AWALAND	120	0.020611
9	0.852830	0.147170	APERSAUT	265	0.045517
10	0.958333	0.041667	ABESAUT	48	0.008245
11	0.927928	0.072072	AMOTSCO	222	0.038131
12	1.000000	0.000000	AVRAAUT	9	0.001546
13	0.907692	0.092308	AAANHANG	65	0.011165
14	0.965035	0.034965	ATRACTOR	143	0.024562
15	1.000000	0.000000	AWERKT	21	0.003607
16	0.979798	0.020202	ABROM	396	0.068018
17	0.921502	0.078498	ALEVEN	293	0.050326
18	0.967742	0.032258	APERSONG	31	0.005325
19	0.842105	0.157895	AGEZONG	38	0.006527
20	0.826087	0.173913	AWAOREG	23	0.003951
21	0.949640	0.050360	ABRAND	139	0.023875
22	0.666667	0.333333	AZEILPL	3	0.000515
23	0.606061	0.393939	APLEZIER	33	0.005668
24	0.897959	0.102041	AFIETS	147	0.025249
25	0.888889	0.111111	AINBOED	45	0.007729
26	0.804878	0.195122	ABYSTAND	82	0.014085

	MOSTYPE	MAANTHUI	MGEMOMV	MGEMLEEF	MOSHOOFD	MGODRK	MGODPR	MGODOV	MGODGE	MRELGE	MRELSA	MRELOV	MFALLEEN	MFGEKIND	MFWEKIND	MOPLHOOG	MOPLMIDD	MOPLLAAG	MBERHOOG	MBERZELF	MBERBOER	MBERMIDD	MBERARBG	MBERARBO	MSKA	MSKB1	MSKB2	MSKC	MSKD	MHHUUR	MHKOOP	MAUT1	MAUT2	MAUT0	MZFONDS	MZPART	MINKM30	MINK3045	MINK4575	MINK7512	MINK123M	MINKGEM	MKOOPKLA	PWAPART	PPERSAUT	PBROM	PBRAND	AWAPART	APERSAUT	ABROM	ABRAND	CARAVAN
307	35	1	4	3	8	1	4	2	3	7	1	2	0	0	9	0	0	9	0	0	0	0	5	4	0	0	0	5	4	9	0	6	2	2	7	2	0	4	5	0	0	4	5	0	6	0	0	0	1	0	0	0
348	35	1	4	3	8	1	4	2	3	7	1	2	0	0	9	0	0	9	0	0	0	0	5	4	0	0	0	5	4	9	0	6	2	2	7	2	0	4	5	0	0	4	5	0	6	0	0	0	1	0	0	0
393	33	1	3	3	8	1	4	2	3	7	1	2	2	3	4	1	3	5	1	1	1	2	3	3	2	2	2	4	1	4	5	6	2	2	7	2	1	4	4	1	0	4	3	2	0	0	4	1	0	0	1	0
537	34	2	3	2	8	0	7	0	2	7	2	0	0	4	5	0	2	7	0	2	0	2	4	2	0	0	4	5	0	2	7	5	4	0	9	0	0	5	4	0	0	4	6	0	0	0	0	0	0	0	0	0
676	33	1	3	3	8	0	4	0	5	9	0	0	0	3	6	1	2	7	1	0	0	3	4	2	1	1	1	6	1	5	4	6	1	2	7	2	2	4	3	1	0	4	3	0	0	0	0	0	0	0	0	0
700	27	2	2	4	6	0	5	2	3	7	0	2	2	5	3	0	1	8	0	0	0	2	4	3	0	0	1	7	2	9	0	8	0	1	7	2	5	4	0	0	0	2	1	0	0	0	2	0	0	0	1	0
707	33	1	2	3	8	0	7	0	2	7	2	0	0	5	4	0	3	6	2	0	0	2	5	2	2	1	2	5	2	0	9	4	4	2	6	3	2	5	3	0	0	3	3	2	0	0	4	1	0	0	1	0
726	33	1	4	2	8	0	5	0	4	8	0	1	0	1	8	0	1	8	0	0	0	1	5	4	0	1	1	7	1	9	0	7	2	0	9	0	3	6	1	0	0	3	3	0	0	0	0	0	0	0	0	0
737	31	1	3	3	7	0	2	3	4	6	0	3	2	0	7	0	3	6	0	0	0	3	3	4	0	2	0	7	0	9	0	7	0	2	9	0	2	5	3	0	0	3	1	0	0	0	0	0	0	0	0	0
738	6	1	3	3	2	1	5	2	3	7	1	2	0	2	7	4	4	2	5	1	0	3	1	1	5	1	2	2	1	1	8	7	0	2	4	5	2	2	4	2	1	5	8	0	6	0	0	0	1	0	0	0
779	38	1	3	3	9	1	4	1	4	7	1	2	2	4	3	1	2	6	4	0	0	2	2	1	2	2	3	3	0	2	7	6	2	1	5	4	2	5	2	2	0	4	3	0	0	0	2	0	0	0	1	0
833	9	1	3	3	3	0	5	0	4	7	0	2	2	4	3	0	7	2	0	0	0	7	0	2	0	3	5	2	0	9	0	9	0	0	9	0	4	5	0	0	0	3	4	0	6	0	0	0	1	0	0	0
867	22	1	1	2	5	0	5	2	2	1	4	5	5	4	1	1	6	2	1	0	0	6	1	3	1	4	3	3	0	9	0	3	2	4	6	3	3	4	3	0	0	3	2	0	0	0	0	0	0	0	0	0
902	39	1	3	3	9	1	6	1	2	5	1	3	0	5	4	0	5	4	0	0	0	0	5	4	0	0	0	9	0	0	9	6	1	3	7	2	3	4	3	1	0	4	5	0	6	0	0	0	1	0	0	0
1013	35	1	3	3	8	0	7	0	2	7	0	2	2	0	7	1	4	5	0	0	0	4	3	3	0	2	2	3	3	7	2	4	1	4	8	1	2	4	4	0	0	3	5	0	0	0	0	0	0	0	0	0
1030	33	1	3	3	8	0	6	1	2	6	0	3	2	3	5	1	2	6	1	0	1	4	2	4	1	2	2	5	2	2	7	7	0	2	7	2	0	4	5	0	0	4	3	0	5	0	0	0	1	0	0	0
1032	33	1	4	3	8	0	6	0	3	9	0	0	0	3	6	0	0	9	0	0	3	0	6	0	0	0	3	6	0	9	0	7	2	0	9	0	3	5	2	0	0	3	3	0	6	0	0	0	1	0	0	0
1063	32	1	3	3	7	0	5	1	3	7	1	2	3	2	5	1	3	6	2	1	1	2	3	3	1	1	1	5	2	5	4	5	2	2	7	2	4	4	1	1	1	3	1	2	0	0	2	1	0	0	1	0
1067	24	1	2	2	5	0	3	1	6	5	2	3	3	2	5	1	4	5	1	0	0	3	4	2	1	2	2	5	1	8	1	6	1	2	8	1	6	3	1	0	0	2	2	2	5	0	2	1	1	0	1	0
1090	13	1	3	4	3	0	2	4	4	7	0	2	4	0	5	4	0	5	2	0	0	4	1	3	0	0	5	3	3	9	0	7	0	2	6	3	6	0	3	0	0	3	6	0	0	0	2	0	0	0	1	0
1122	13	2	4	3	3	0	4	2	4	8	1	1	1	3	6	1	7	2	4	0	0	3	3	0	1	3	3	3	0	1	8	8	0	1	4	5	0	5	4	0	0	4	6	0	0	0	3	0	0	0	1	0
1174	33	1	3	3	8	0	9	0	0	5	0	4	2	3	4	0	3	6	0	0	2	0	3	5	1	0	0	7	2	4	5	3	2	4	7	2	6	2	2	0	0	2	3	0	6	0	0	0	1	0	0	0
1199	33	1	3	2	8	0	4	2	4	8	1	0	0	2	7	0	3	6	0	0	0	4	3	3	0	1	3	6	0	3	6	6	3	0	9	0	2	5	2	2	0	4	3	2	6	0	4	1	1	0	1	0
1238	33	1	3	3	8	0	4	1	4	7	0	2	2	3	5	0	2	7	0	0	3	0	3	3	2	0	2	3	4	3	6	5	2	3	7	2	5	4	0	0	0	2	3	0	0	0	0	0	0	0	0	0
1256	37	1	2	3	8	0	4	1	4	6	1	3	1	5	3	1	5	3	1	0	1	2	1	5	1	1	5	3	1	3	6	4	0	5	7	2	3	3	1	3	1	5	4	0	0	0	4	0	0	0	2	0
1293	8	1	3	3	2	1	5	1	3	7	1	2	2	3	5	2	4	4	3	1	2	2	2	1	3	2	3	3	1	3	6	6	2	1	5	4	3	2	3	2	1	4	7	0	0	0	0	0	0	0	0	0
1304	27	2	2	5	6	1	5	3	1	7	0	2	2	6	1	2	4	4	2	1	2	2	0	3	1	1	5	2	0	8	1	8	0	1	7	2	2	7	1	0	0	3	1	0	6	0	0	0	1	0	0	0
1319	12	2	4	2	3	0	2	0	7	9	0	0	0	0	9	0	3	6	0	0	0	7	0	2	0	2	0	6	2	0	9	9	0	0	9	0	2	0	5	2	0	5	7	2	6	0	4	1	1	0	1	1
1322	38	1	2	4	9	1	5	2	1	6	1	3	3	4	3	2	5	3	2	0	0	5	1	2	1	2	2	4	1	7	2	5	1	3	6	3	5	2	2	1	0	3	4	0	0	0	0	0	0	0	0	0
1326	32	2	2	4	7	0	3	3	4	5	1	4	2	5	2	1	4	5	2	1	0	1	5	2	1	1	4	4	1	3	6	7	1	2	7	2	5	3	1	1	0	3	1	2	0	0	2	1	0	0	1	0
1336	30	1	2	4	7	0	4	0	5	3	0	6	6	2	2	1	1	7	0	0	0	0	5	4	0	0	0	6	3	9	0	5	0	4	9	0	6	3	0	0	0	2	2	0	6	0	0	0	1	0	0	0
1368	33	2	3	3	8	0	9	0	0	9	0	0	0	2	7	0	3	6	3	0	0	2	5	0	2	2	2	5	0	0	9	6	3	0	3	6	0	5	4	0	0	4	3	2	0	0	2	1	0	0	1	0
1371	34	2	3	2	8	0	7	0	2	7	2	0	0	4	5	0	2	7	0	2	0	2	4	2	0	0	4	5	0	2	7	5	4	0	9	0	0	5	4	0	0	4	6	2	6	0	4	1	1	0	1	0
1375	31	1	3	2	7	2	2	0	5	6	1	3	2	2	6	0	3	6	1	0	2	0	4	4	0	0	5	4	1	7	2	6	0	3	9	0	7	2	0	0	0	2	1	0	0	3	0	0	0	1	0	0
1463	13	1	3	2	3	1	5	1	3	8	1	1	1	3	6	4	4	2	4	1	0	3	1	1	3	2	3	2	0	1	8	7	1	1	4	5	1	3	4	2	1	5	6	0	0	0	0	0	0	0	0	0
1470	11	2	2	5	3	2	4	4	0	5	0	4	4	2	3	2	3	4	4	0	0	2	0	4	4	2	0	0	4	0	9	4	2	4	5	4	3	5	1	1	0	3	6	2	6	0	4	1	1	0	1	0
1476	33	1	3	3	8	1	4	2	3	7	1	1	1	4	4	1	2	7	2	1	1	1	4	2	2	1	2	5	1	2	7	4	4	2	7	2	3	3	3	0	1	4	3	0	6	0	0	0	1	0	0	0
1500	35	1	3	3	8	0	7	0	2	7	0	2	2	0	7	1	4	5	0	0	0	4	3	3	0	2	2	3	3	7	2	4	1	4	8	1	2	4	4	0	0	3	5	0	0	0	0	0	0	0	0	0
1526	24	1	3	2	5	1	3	2	4	6	1	2	2	3	5	0	4	5	2	0	0	0	5	3	0	1	4	3	1	4	5	5	1	3	7	2	2	5	3	0	0	3	2	0	0	0	0	0	0	0	0	0
1543	36	1	2	4	8	1	6	0	2	7	0	2	0	5	4	0	0	9	5	0	0	0	0	4	0	0	0	5	4	9	0	6	0	3	7	2	0	4	5	0	0	4	3	0	0	0	0	0	0	0	0	0

	index	percent	CARAVAN
0	0	94.022673	5474
1	1	5.977327	348